PM2.5 exposure disparities persist despite strict vehicle emissions controls in California

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California’s aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity.


Text S2. Validation and Comparison to Other Estimates
Here, we aim to validate our results with previously published observations and model data.The observational record lacks the spatial, temporal, and chemical information to track the long-term evolution of primary and secondary PM2.5 contributions attributable to vehicles only across California at sufficient spatial resolution to quantify disparities.Accordingly, we interrogate the plausibility of our results by considering three fundamental questions that we can individually validate with independent peer-reviewed estimates of ambient PM2.5, NO2, and diesel particulate matter.First, are the changes we find in statewide PWM exposures from onroad mobile sources consistent with California's ambient total PM2.5 and NO2 concentrations?Second, are the changes in exposure and relative disparity in exposure by race-ethnicity comparable with estimated disparity by race-ethnicity for total PM2.5 and NO2?Finally, are the changes we model in overburdened communities consistent with state regulatory monitoring in these communities?
For this discussion, we analyze two external datasets and briefly review observations from the literature.The first dataset is the annual mean PM2.5 concentration derived from satellite observations gridded at 0.01 o by 0.01 o (average ~1 km 2 in California, 58).The second dataset is from the Center for Air, Climate, and Energy Solutions (59), which predicts concentrations of PM2.5 and NO2 via an empirical (land use regression) model with high fidelity as compared to monitoring sites (R 2 = 0.84 and 0.81 for PM2.5 and NO2, respectively).NO2 is a useful analog for mobile source PM2.5, as on-road mobile sources are a major source of NOx emissions in California (in year 2000, 58% of NOx came from on-road sources) (39).In contrast, on-road mobile sources are only a small fraction (<10%) of California's estimated primary PM2.5 emissions (39), and a larger, but not dominant, source of total PM2.5 concentrations according to source apportionment studies (67)(68)(69)(70)(71)(72).
We first contextualize the magnitude of our on-road mobile source PWM PM2.5 concentration (relative to total PWM PM2.5) against evidence from the literature.Our estimates of on-road mobile source PWM PM2.5 represent approximately ~10-20% of the total PWM PM2.5 derived from satellites or empirically modeled (58,59).This result is broadly in line with the available evidence from other modeling studies and in-situ source apportionment studies, which generally attribute ~10-25% of ambient PM2.5 to mobile sources depending on the setting and study year (67)(68)(69)(70)(71)(72).
Next, we find broadly similar temporal changes indicating sharply reduced concentrations but persistent relative disparity when considering these two gridded datasets.We recreate the analysis of PWM concentration over time (cf.Fig. 1a) using satellite derived PM2.5 (Fig. S3A) and the empirically modeled PM2.5 (Fig. S3B) and NO2 (Fig. S3C) estimates.The reduction in total statewide PWM PM2.5 (~45% reduction based on satellite and empirically derived estimates) is less than the reduction in our estimated mobile-source reduction (~65% reduction).This result makes sense -models and in-situ source-apportionment observations alike indicate that on-road mobile sources contribute only a fraction of ambient PM2.5 in California, but mobile-source emissions of PM2.5 and its precursors have declined more rapidly.In contrast, the magnitude of empirically modeled PWM NO2 (~55% reduction) more closely resembles our result for the mobile source PM2.5 reduction; this finding is reasonable given that mobile sources account for a substantially larger fraction of ambient NO2 than ambient PM2.5.We then computed relative disparities from modeled and remotely sensed measurements of total PM2.5 and NO2 (Fig. S3B) to provide a point of comparison with Fig. 1b.Qualitatively, these results align closely with our core finding: relative disparities by race/ethnicity for total PM2.5 and NO2 persisted over the 2000-2019 time period.Considering the two PM2.5 datasets, we found slight reductions in relative disparity (e.g., reduction from ~9-10% to 7-8% for Hispanic Californians from 2000 -2019).For empirically modeled total NO2, relative disparities by raceethnicity increased slightly (e.g., for Hispanic Californians, increasing from 14 to 15%), which is broadly consistent with our finding of modestly increasing disparities for PM2.5 from mobile sources (from 12-14%).Overall, the similarity between the relative disparity in exposure to NO2 from the empirical model and the relative disparity in exposure to on-road mobile source PM2.5 we estimate here instills confidence in our finding.
As a final point of comparison for overburdened communities, we consider a 2016 California Air Resources Board report on air quality in overburdened communities (60).Regulatory measurements of elevated concentrations of diesel particulate matter, PM2.5, and NO2 in overburdened communities between 2000 and 2014 support our finding that there is sustained disparity in overburdened communities during our study period (60).Additionally, they estimated an approximate 65% reduction in diesel particulate matter concentrations in overburdened communities from 2000 to 2014.While we do not explicitly model diesel particulate matter, we can approximate exposure to diesel particulate matter by isolating our estimate of PWM exposure to exhaust-only primary PM2.5 from HDVs.For members of overburdened communities, our results are generally consistent with the findings from observations.From 2000-2014, our modeled results show PWM PM2.5 reductions of 73% and 72% for AB617 and SB535 residents, respectively.In summary, available data from CARB regulatory monitoring supports our conclusions for overburdened communities.

Text S3. Model Uncertainty and Sensitivity
While the previous section validates our results against previous work, here we aim to discuss the potential uncertainties and bias associated with our modeling approach.The results and conclusions in this work are based on modeled concentrations from the ISRM (28, 73) using state regulatory emissions modeled from EMFAC (38).InMAP and the ISRM have been validated against measurement and comprehensive chemical-transport models to adequately estimate population-weighted mean exposures (28,73).However, as with any model, results are potentially influenced by uncertainty, error, and bias.In this section, we discuss the sensitivity of our finding of persistent disparity to error and bias in the emissions estimates, reducedcomplexity model, and population dataset.

Emissions Inventory Uncertainty and Sensitivity
With respect to the emissions inventory, while EMFAC is the state regulatory model for on-road mobile source emissions, emissions estimates can introduce error and bias that could influence our results (74).For completeness in this discussion, we decompose the emissions uncertainty in magnitude (relevant for our finding that population-weighted mean exposure has decreased for all Californians) and in space (relevant for our finding that disparities have persisted).Toward validation of our estimated changes in emissions over time, Yu et al. (50) found that EMFAC reasonably predicts the decrease in mobile-source NOx emissions in California for gasoline and diesel vehicles when compared with a fuel-based inventory.While the spatial distribution of emissions from EMFAC have not been formally validated in the literature, the spatial surrogates used to allocate emissions to the 1 km modeling grid use carefully compiled and highly resolved data from other California state agencies.To help constrain uncertainty in our results due to potential spatial inaccuracies in emissions allocation, we consider analogous mobile source inventories with activity-based spatial surrogates.A study of a similar on-road mobile source emissions model (Neighborhood Emission Mapping Operation, NEMO), for example, found high overall spatial correlation but potential underestimation of emissions inequality between satellite-derived estimates of NO2 and modeled NOx emissions (74).In Fig. 16A, we repeat our analysis using two independently derived and peer-reviewed emissions inventories: EDGAR (v6.1,78) and NEMO (v2017, 91).As shown in Fig. 16A, while the relative disparities in exposure estimated using the coarser emissions from EDGAR are lower for all groups, the relative disparity in exposure is sustained.Similarly, the relative disparity in exposure when modeled with emissions from a single available year of data (2017) from NEMO are consistent with our overall findings.In Fig. 16B, we show that the highresolution spatial emissions allocation are not overly influencing our results.We repeat our analysis after re-gridding the EMFAC emissions to make our inventory coarser in space.Even with 4 km resolution emissions estimates, we find that exposure disparities persisted.Additionally, while EMFAC could have biases in its spatial emissions allocation, we believe our overall assessment of relative disparities for PM2.5 from on-road sources is robust, given the consistency in magnitude and direction of our results with disparities in empirically modeled NO2 concentrations (Fig. S3).Therefore, while there may be bias or uncertainty in the emissions inventory, we do not expect it to qualitatively affect our conclusions.

Reduced-Complexity Model Uncertainty and Sensitivity
A useful framework for discussing the uncertainty of the reduced-complexity model decomposes the model accuracy into two important axes: fidelity of the overall magnitude of exposure concentrations, and fidelity of the spatial distribution of the exposure concentrations.Because our results rely on the spatial relationships between population groups and exposure concentrations, inaccuracies in the magnitude of overall concentration will perturb the population-weighted mean concentrations but will not affect the exposure disparities.Accordingly, we focus on potential spatial inaccuracies in the model.To properly assess the effects of model error and bias on our conclusions, here, we perform two sensitivity analyses on our estimated relative disparities in exposure.
First, we show that our result is robust against potential spatial and demographically linked biases in the model (Fig. S16B).To do this, we iterate through each of the racial-ethnic groups and perturb the concentrations by ±18% (the mean fractional error for InMAP versus WRF-Chem, 73) for cells where 50% or more of the population is of that specific racial-ethnic group.We then estimate the PWM for the individual group and the full population, from which we estimate the relative disparity.The relative disparity under the perturbed conditions for each group are shown as error bars in Fig. S16B.As shown in Fig. S16B, we find that even if model errors were spatially biased in a manner that was substantially correlated with the racial-ethnic population distribution, disparities persist.While there is now some overlap between the error bars for the relative disparity in exposure for Hispanic, Black, and Asian Californians, relative disparities between Californians of color and white Californians persist, even when concentrations are underestimated in communities of color and overestimated in white communities.
Second, we test the sensitivity of our conclusion to potential errors in model performance associated with individual precursor pollutants.In Fig. S16C, we perform a systematic leaveone-out analysis on relative disparity for each precursor pollutant.To do this, we estimate PWM exposures for each racial-ethnic group from the total PM2.5 concentrations (Fig. 1B) and each combination of four precursors.The relative disparity in exposure to total PM2.5 is represented by a point, the minimum and maximum relative disparity caused by leaving out a single precursor create the error bars, and the relative disparity from secondary PM2.5 only is indicated by a star.Again, we find that our results are robust to model error associated with any one of the precursor pollutants.

Sensitivity to Population Dataset
In our core analyses, we rely on static population data for year 2010, the midpoint of our 2000 -2019 analysis time frame.As a sensitivity analysis (Fig. S17), we also considered how our results would change using a year-2000 population estimate, which reflects the only other year for which a reliable demographic estimate is available for California from the decennial census.From 2000 to 2010, California's Hispanic population grew by 28% while the white population decreased by approximately 5%, resulting in an overall larger share of the population identifying as Hispanic (32.4% Hispanic in 2000 and 37.6% Hispanic in 2010).Additionally, population migration occurred for all racial-ethnic groups in California, moving away from coastal cities toward more suburban or rural inland areas and decreasing overall segregation (92).We show that our finding that PWM exposure has decreased while relative disparity has persisted is recreated when using a static 2000 population dataset instead of a static 2010 population dataset.While the changes across time and demographic group are consistent, one difference between our main result (Fig. 1) and the result of this sensitivity test (Fig. S17) is the overall magnitude of the PWM.The higher estimated PWM using a static 2000 population dataset is consistent with the two aforementioned population changes that occurred in California from 2000 to 2010.First, the lower PWM using 2010 population data is consistent with the population migration shifts away from cities, major roadways, and highways.Second, the increased fraction of California's population that is Hispanic affects the statewide total PWM.As the Hispanic share of the population increases, the statewide and Hispanic PWMs may converge over time, which will ultimately affect the relative disparity metric (for this sensitivity test, we observed only a modest impact of this effect).Nonetheless, comparing the sensitivity of our results to the exposure for members of overburdened communities, we find <2% difference in PWM and relative disparity across all years (e.g., PWM in 2000 for members of AB617 communities of 4.5 μg/m 3 using 2000 Census data and 4.4 μg/m 3 using 2010 Census data).Together, the similarities in exposure for members of overburdened communities and the shape of the PWM and relative disparity results suggest that the changes in the spatial distribution of emissions is a more important driver of disparity than the changes in the spatial distribution of the population.

Fig. S2. Maps of overburdened communities in California.
The overburdened communities and geographic regions evaluated as part of this work are identified on two maps of California.(A) AB617 overburdened communities are marked by a circle with their names italicized.We also highlight three broad geographic regions (San Francisco Bay Area, Central Valley, Los Angeles Area), which we defined in terms of the counties associated with each region.(B) The SB535 overburdened communities are shaded in red.Inset maps show more detailed views into the San Francisco and Los Angeles metropolitan areas.While SB535 also includes tribal community areas, in this work we focus on the Census Tracts.All results computed for SB535 are at the statewide population-weighted mean.Because the population living in federally recognized tribal lands included in SB535 contribute a relatively small number of people, we do not anticipate that our results would change substantially upon inclusion of these areas.

Fig. S3. Validation analyses to compare modeled results to other lines of evidence.
Here, Fig. 1 is reproduced with two additional data sets derived from satellite observations of PM2.5 and an empirical model (land use regression) for PM2.5.andNO2.In panels (A) and (B), we estimate the PWM PM2.5 exposure for each racial-ethnic group and the statewide population using estimates derived from satellite observations and empirical model, respectively.In panel (C), we estimate the PWM NO2 exposure for each racial-ethnic group using estimates from the empirical model.This figure accompanies Fig. 3 (disparity for Hispanic Californians) by illustrating the disparity in exposure for each additional racial-ethnic demographic group.As with Fig. 3, we present three ways to disaggregate contributions from LDVs, MDVs, HDVs, and all other vehicles.The absolute disparity is estimated as the total population-wide on-road mobile-source exposure subtracted from the population-weighted mean exposure (A-D).The fractional contribution to disparity divides the absolute source contribution by the total absolute disparity (E-H).The relative disparity to each on-road mobile source compares an individual group's exposure to each individual vehicle type to the statewide average exposure to that vehicle group (I-L).Asian Californians (leftmost column) are the only racial-ethnic group that does not experience uniformly positive or negative disparity in exposure from all fleets.In other words, unlike all other racial/ethnic groups, Asian Californians are more exposed to LDVs, MDVs, and all other vehicles than the statewide average Californian but less exposed to HDVs.As a result, the contribution to disparity in panel (E) only shows the fractional contribution to the higher-thanstatewide exposure disparity and HDVs are not included.For Black Californians (second column from the left), the relative contribution and source-specific relative disparity are very similar to those of Hispanic Californians (Fig. 3).White (second column from the right) and other (rightmost column) Californians are less exposed to all on-road mobile vehicles than statewide average, so note the opposite orientation of disparity in panels (C-D) and (K-L).This figure accompanies Fig. 3 (disparity for Hispanic Californians) by illustrating the disparity in exposure for residents of the two classifications of overburdened communities.The disparity in exposure for residents of overburdened communities as defined in AB617 (lefthand column) and SB535 (righthand column) is broken down in three ways to disaggregate contributions from LDVs, MDVs, HDVs, and all other vehicles following the same methodology as Fig. 3 and Fig. S9.The fractional contributions to disparity from each fleet type for AB617 and SB535 community members (A-D) on aggregate closely mirror that for Hispanic and Black Californians.However, the relative disparity from each mobile source is slightly different for AB617 community members (E), as they are most disparately exposed to PM2.5 resulting from emissions from LDVs.Contribution to total PM2.5 concentration from emissions of each precursor pollutant from the full vehicle fleet across time by racial-ethnic group.Here, we perform a similar analysis to Fig. 3 of disaggregating results, but we show concentration from each individual precursor pollutant rather than fleet type.Contributions from primary PM2.5 emissions are split by emissions mode, using hatching to represent non-exhaust (brake wear, tire wear) PM2.5 emissions.The shape and contribution of each precursor does not change substantially between the statewide total population (A) and each racial-ethnic group (B-F).The relative contribution of each species is also roughly even across time and racial-ethnic group.From the LDV fleet, emissions of NOx, VOC, and NH3 are most important for populationweighted mean exposures to total PM2.5.During the second half of the study, the importance of exhaust-and non-exhaust primary PM2.5 switch, leading to a roughly constant overall contribution from primary PM2.5.

Fig. S13. Population-weighted mean exposure by precursor from the MDV fleet.
Contribution to total PM2.5 concentration from emissions of each precursor pollutant from the MDV fleet across time by racial-ethnic group.Here, we repeat the analysis from Fig. S11 but for the MDV fleet alone.Again, the shape and contribution of each precursor does not change substantially between the statewide total population (A) and each racial-ethnic group (B-F).From the MDV fleet, emissions of primary PM2.5 and NOx are most important for populationweighted mean exposures to total PM2.5.While relatively less important, contributions from NH3 are increasing in the second half of the study, while contributions of VOC are declining.

Fig. S14. Population-weighted mean exposure by precursor from the HDV fleet.
Contribution to total PM2.5 concentration from emissions of each precursor pollutant from the HDV fleet across time by racial-ethnic group.Here, we repeat the analysis from Fig. S11 but for the HDV fleet alone.Again, the shape and contribution of each precursor does not change substantially between the statewide total population (A) and each racial/ethnic group (B-F).From the HDV fleet, emissions of primary PM2.5 and NOx are far more important for populationweighted mean exposures to total PM2.5.During the second half of the study, the importance of VOC and NH3 switch, with NH3 becoming increasingly important for total PM2.5 formation from HDVs.Similarly, while still important for HDV-derived PM2.5, contributions from exhaust-mode primary PM2.5 have reduced, resulting in a higher fractional contribution from NOx.

Fig. S15. Population-weighted mean exposure per unit emission for each fleet and racialethnic group.
We compare the per-unit impact of emissions of each precursor species from each vehicle type on population-weighted mean (PWM) exposure concentration for each race-ethnicity.Differences in this metric arise principally because of source patterns of proximity (i.e., on average, which vehicle types drive nearest to each group of people).This metric also depends on patterns of atmospheric transport and chemistry, but these patterns do not have a strong relationship with demographics.The box plots represent the distribution of this metric across the 19 years.The median is represented by the central bar, the mean is shown as a circle, the upper and lower quartiles are represented by the box, and the whiskers show the 10th and 90th percentile.For each vehicle type, the PWM concentration per unit emissions experienced by Black and Hispanic Californians is substantially higher than that experienced by white Californians, reflecting in large part the differences in proximity to vehicle activity.We see especially large exposures per unit of emissions of LDV emissions because LDV emissions tend to be ubiquitously concentrated in residential areas.In broad contours, these results are very similar for each of the emitted species.

Fig. S16. Sensitivity analyses demonstrate our finding of persistent relative disparities over time is robust to a wide range of potential biases and uncertainties in emissions inventory and model performance.
Here, Fig. 1B is reproduced with error bars that show the sensitivity to four perturbations.The sensitivity tests are described in detail in the "Model Uncertainty and Sensitivity" subsection of the Supplementary Text.In Fig. S16A, we estimate relative disparity in exposure to emissions from two independent emissions inventories.The one-directional error bars indicate the relative disparity in exposure to emissions from the EDGAR model (gridded at 0.1 o , or ~10 km); the relative disparity in exposure to emissions from the NEMO model (gridded at 1 km), which are only available for 2017, are indicated by a star.Regardless of the emissions model chosen, the relative disparities in exposure persist across time.In Fig. S16B, we show that our results are not influenced by error introduced by fine-scale spatial allocation.To do this, we re-grid the EMFAC emissions to a 2 km and 4 km grid.The error bars represent the relative disparity in exposure when emissions are gridded at 4 km and show minimal change in the magnitude or temporal pattern of disparity.In Fig. S16C, we perform a series of demographically linked perturbations to the modeled concentrations to simulate how spatial bias in the model could affect relative disparities in exposure.Error bars represent the range of relative disparities in exposure for each racial-ethnic group when concentrations are perturbed by ±18% in grid cells with 50% or more of the population.As shown in Fig. S16C, relative disparities in exposure persist, even if concentrations in predominantly Hispanic, Black, and Asian areas are overestimated and concentrations in predominantly white areas are underestimated.In Fig. S16D, we perform a systematic exclusion of each precursor pollutant and confirm that our results are robust to any model error associated with an individual precursor.The scenario where the excluded precursor is primary PM2.5 (i.e., all PM2.5 is secondary) is denoted with a star and represents the smallest relative disparity for all groups.Here, Fig. 1 is recreated using a static 2000 Census population instead of the static 2010 dataset used throughout this manuscript to evaluate the sensitivity of our finding to the population data.The main findings previously described have not changed: even with a static 2000 population, the exposure concentrations have decreased for all racial-ethnic groups (A) but the relative disparity in exposures have persisted (B).A key difference is that the overall populationweighted mean exposure concentration when using 2000 Census data is slightly higher for Californians of color, resulting in a higher relative disparity in exposure.This finding may suggest a population migration of people of color towards lower traffic areas between the 2000 and 2010 Census.Assuming the trend of population migration continued towards 2020, we might expect that the PWM exposure concentrations for each group might decrease further towards 2019.However, the persistence of the relative exposure disparity regardless of population dataset year suggests that the spatial distribution of emissions is not markedly variable over time, thereby leading to relatively constant relative disparities over time.Statewide population-weighted mean exposure concentrations (A) and relative disparity in exposure (B) to emissions of only non-exhaust PM2.5 from on-road mobile sources during the study period.The Asian, Black, white, and other racial groups do not include those identifying as Hispanic.Here, we show that the population-weighted mean exposure to emissions of nonexhaust PM2.5 has increased for all groups yet the relative disparity in exposure has remained constant.Notably, although the exposure concentration from non-exhaust sources is only approximately 1% of the current National Ambient Air Quality Standard, the magnitude of the relative disparity in exposure for all groups is larger for non-exhaust PM2.5 than for the full set of emissions shown in Fig. 1.While this is not equivalent to modeling the future all-electric fleet, we can use the non-exhaust emissions from the full vehicle fleet as an analog for current driving patterns and emissions that might be relevant to disparities in an all-electric future.This sensitivity analysis suggests that relative disparities may persist even in the "all-electric future" where all vehicles across the fleet have zero exhaust emissions.b.The Asian, Black, white, and other groups include only people who do not identify as Hispanic.The Hispanic group includes members of all races who identify as Hispanic.c. Population by race and ethnicity at the tract level was apportioned to community boundaries defined by the California Air Resource Board using area allocation.d.Population by race and ethnicity at the tract level was filtered to those tracts included in SB535.

Table S3.
Exposure and disparity to on-road vehicles by race-ethnicity.a.The population-weighted mean exposure is estimated by multiplying the annual average PM2. 5 concentration by the population of the demographic group of interest within that grid cell, summing across all grid cells, and dividing by the total population:

Racial-Ethnic Group
A positive number indicates a higher than statewide average exposure disparity.Numbers shown here may not subtract correctly due to rounding.
c.The absolute disparity for a racial/ethnic group is estimated as the statewide average exposure ("Total") subtracted from the group's exposure: DA,k = PWMk -PWMT  a.The population-weighted mean exposure is estimated by multiplying the annual average PM2.5 concentration by the population of the demographic group of interest within that grid cell, summing across all grid cells, and dividing by the total population: The communities are identified using abbreviated codes.These codes are as follows: -

Notes:
a.The population-weighted mean exposure is estimated by multiplying the annual average PM2.5 concentration by the population of the demographic group of interest within that grid cell, summing across all grid cells, and dividing by the total population: Members of overburdened communities as designated by California's AB617 and SB535 are included under their respective policy names.

Fig S1 .
Fig S1.Emissions of PM2.5 precursors by on-road mobile source fleet relative to 2000.Changes in total statewide emissions of each on-road mobile vehicle pollutant modeled as a precursor for total PM2.5 concentration stratified by fleet type.Total statewide emissions are shown relative to the emissions in 2000.In the upper righthand corner of each panel, the approximate percent contribution to secondary PM2.5 concentration is shown.For primary PM2.5 emissions (A), emissions are split by mode using hatching to represent the non-exhaust fraction (brake wear and tire wear).Emissions of SOx (E) are relatively unimportant, as the very low SOx emissions results in negligible secondary PM2.5.Emissions of primary PM2.5, NOx, and VOC have declined substantially from 2000 -2019.Emissions of NH3 (C) were declining in the first decade, but widespread catalytic conversion usage has led to a modest increase in emissions during the second decade of the study.For NH3 and VOC (C-D), LDVs are responsible for the majority of statewide emissions; for PM2.5 and NOx, LDVs and HDVs contribute approximately evenly.The contribution of each precursor to total PM2.5 concentration is explored further in Figs.S11-S14.
Fig. S3.Validation analyses to compare modeled results to other lines of evidence.Here, Fig.1is reproduced with two additional data sets derived from satellite observations of PM2.5 and an empirical model (land use regression) for PM2.5.andNO2.In panels (A) and (B), we estimate the PWM PM2.5 exposure for each racial-ethnic group and the statewide population using estimates derived from satellite observations and empirical model, respectively.In panel (C), we estimate the PWM NO2 exposure for each racial-ethnic group using estimates from the empirical model.Fig.1Bis reproduced for each of these datasets in panels (D) through (F).The gray shading in panels (A), (B), (C), and (D) superimpose the results by race-ethnicity from Fig.1directly.As discussed in the "Validation and Comparison to Other Estimates" supplementary text, the consistency in the trends in ambient PWM and relative disparity in exposure here support the robustness of our findings.

Fig. S4 .
Fig. S4.On-road mobile-source PM2.5 exposure and relative disparity in exposure for additional demographic variables.Statewide population-weighted mean PM2.5 exposure concentrations and relative disparity in exposure attributable to on-road mobile sources are shown for two additional demographic variables.In the top row, we show the (A) population-weighted mean PM2.5 exposure concentration and (B) relative disparity for the four quartiles of CalEnviroScreen score relative to the two groupings of California's overburdened communities described in the main text (AB617 and SB535, "disparity by policy").In the second row, we show these same metrics for four quartiles of California's income distribution (C, D).Income quartiles are defined as follows.Total population and median household income (adjusted to 2012 inflation) were queried at the block group level from the 2008-2012 American Community Survey.Income data were adjusted to 2010 using the Consumer Price Index.Block groups were then sorted by median income level and the percentile of the overall population was estimated as the cumulative number of people at that income level divided by the total population: < $40,892 Q1, $40,893-$58,452 Q2, $58,453-$82,842 Q3, and >$82,843 Q4.Each quartile represents approximately 9.3 million Californians.In each year, relative exposure disparities (B, D) for each group are computed in reference to statewide average PM2.5 concentration attributable to on-road mobile sources.

Fig. S5 .
Fig. S5.Distributions of exposure to mobile-source PM2.5 for racial-ethnic groups.Variation in PM2.5 exposure distributions from on-road vehicle emissions for racial-ethnic groups in California in 2000 (lefthand column), 2010 (center column), and 2019 (righthand column).The rows represent each of the vehicle fleets: all vehicles (A-C), LDVs (D-F), MDVs (G-I), and HDVs (J-L).Boxplots are roughly centered around the means and indicate the following population-weighted statistics: median (central bar), mean (circle), 25th and 75th percentile (box), and 10th-90th percentiles (whiskers).Across the full exposure distributions and across time, white Californians experience among the lowest exposures to PM2.5 from on-road mobile sources, while Hispanic and Black Californians experience among the highest exposures.Comparing the disparity at the most exposed (90th percentile) Hispanic Californians versus white Californians, the disparity is the most severe in exposure to HDVs.Overall, we note the roughly similar patterns by race/ethnicity for each vehicle fleet type.

Fig. S6 .
Fig. S6.Cumulative distributions of exposure.Cumulative distributions of exposure are drawn for each vehicle fleet for each representative year.Cumulative distributions are estimated by finding the exposure concentration that corresponds to a uniform distribution of California's population, consistent with the methodology used to create Lorenz curves for income or wealth distributions.The leftmost column shows emissions for 2000, the center column represents emissions in 2010, and the rightmost column shows the result of emissions in 2019.Distributions are drawn for each fleet type: (A-C) all vehicles, (D-F) LDVs, (G-I) MDVs, (J-L) HDVs.Across all years and fleet types, the top decile of the population receives over 20 percent of the total population-weighted exposure and the top 25% of the population receives 35-40% of the total population-weighted exposure.

Fig. S7 .
Fig. S7.Racial-ethnic population distribution by exposure decile across fleets in 2010.The distribution of race-ethnicity for the population within each decile of (A) full fleet, (B) LDVs, (C) MDVs, and (D) HDV exposure.The statewide racial-ethnic composition is shown for comparison.As shown here, the results using emissions in 2010 do not substantially vary from the results using 2000 or 2019 emissions shown in Fig. 2, although the magnitude of exposure is different.

Fig. S8 .
Fig. S8.Distribution of exposure reductions by race-ethnicity.Statewide (A) absolute and (B) percent changes in PM2.5 concentration from 2000 to 2019 from the full on-road mobile fleet are binned into deciles and the racial-ethnic composition of each decile of change is shown in comparison to the full statewide population.The left column (A) estimates absolute changes in concentration, following the methodology from recent vehicle electrification studies (e.g., 57, 71).(A) While the grid cells with the largest absolute reduction in concentration consist of more people of color than the statewide average (thus contributing to the reduction in absolute exposure disparity), (B) the smaller demographic differences in the percentage change in exposure highlight the persistent inequalities despite larger absolute reductions in communities of color.The difference between (A) and (B) arises in large part because the geographies with the largest absolute reductions in PM2.5 exposure from on-road mobile sources started out with the highest initial levels of exposure in 2000.

Fig. S9 .
Fig. S9.Contributions to disparity in exposure to mobile-source PM2.5 for each raceethnicity.This figure accompanies Fig.3(disparity for Hispanic Californians) by illustrating the disparity in exposure for each additional racial-ethnic demographic group.As with Fig.3, we present three ways to disaggregate contributions from LDVs, MDVs, HDVs, and all other vehicles.The absolute disparity is estimated as the total population-wide on-road mobile-source exposure subtracted from the population-weighted mean exposure (A-D).The fractional contribution to disparity divides the absolute source contribution by the total absolute disparity (E-H).The relative disparity to each on-road mobile source compares an individual group's exposure to each individual vehicle type to the statewide average exposure to that vehicle group (I-L).Asian Californians (leftmost column) are the only racial-ethnic group that does not experience uniformly positive or negative disparity in exposure from all fleets.In other words, unlike all other racial/ethnic groups, Asian Californians are more exposed to LDVs, MDVs, and all other vehicles than the statewide average Californian but less exposed to HDVs.As a result, the contribution to disparity in panel (E) only shows the fractional contribution to the higher-thanstatewide exposure disparity and HDVs are not included.For Black Californians (second column from the left), the relative contribution and source-specific relative disparity are very similar to those of Hispanic Californians (Fig.3).White (second column from the right) and other (rightmost column) Californians are less exposed to all on-road mobile vehicles than statewide average, so note the opposite orientation of disparity in panels (C-D) and (K-L).

Fig. S10 .
Fig. S10.Contributions to disparity in exposure to mobile-source PM2.5 for residents of overburdened communities.This figure accompanies Fig.3(disparity for Hispanic Californians) by illustrating the disparity in exposure for residents of the two classifications of overburdened communities.The disparity in exposure for residents of overburdened communities as defined in AB617 (lefthand column) and SB535 (righthand column) is broken down in three ways to disaggregate contributions from LDVs, MDVs, HDVs, and all other vehicles following the same methodology as Fig.3and Fig.S9.The fractional contributions to disparity from each fleet type for AB617 and SB535 community members (A-D) on aggregate closely mirror that for Hispanic and Black Californians.However, the relative disparity from each mobile source is slightly different for AB617 community members (E), as they are most disparately exposed to PM2.5 resulting from emissions from LDVs.

Fig. S11 .
Fig. S11.Population-weighted mean exposure by precursor from the full mobile fleet.Contribution to total PM2.5 concentration from emissions of each precursor pollutant from the full vehicle fleet across time by racial-ethnic group.Here, we perform a similar analysis to Fig.3of disaggregating results, but we show concentration from each individual precursor pollutant rather than fleet type.Contributions from primary PM2.5 emissions are split by emissions mode, using hatching to represent non-exhaust (brake wear, tire wear) PM2.5 emissions.The shape and contribution of each precursor does not change substantially between the statewide total population (A) and each racial-ethnic group (B-F).The relative contribution of each species is also roughly even across time and racial-ethnic group.

Fig. S12 .
Fig. S12.Population-weighted mean exposure by precursor from the LDV fleet.Contribution to total PM2.5 concentration from emissions of each precursor pollutant from the LDV fleet across time by racial-ethnic group.Here, we repeat the analysis from Fig.S11but for the LDV fleet alone.Again, the shape and contribution of each precursor does not change substantially between the statewide total population (A) and each racial-ethnic group (B-F).From the LDV fleet, emissions of NOx, VOC, and NH3 are most important for populationweighted mean exposures to total PM2.5.During the second half of the study, the importance of exhaust-and non-exhaust primary PM2.5 switch, leading to a roughly constant overall contribution from primary PM2.5.

Fig. S17 .
Fig. S17.On-road mobile-source PM2.5 exposure and relative disparity in exposure for each demographic group using static 2000 population data.Here, Fig.1is recreated using a static 2000 Census population instead of the static 2010 dataset used throughout this manuscript to evaluate the sensitivity of our finding to the population data.The main findings previously described have not changed: even with a static 2000 population, the exposure concentrations have decreased for all racial-ethnic groups (A) but the relative disparity in exposures have persisted (B).A key difference is that the overall populationweighted mean exposure concentration when using 2000 Census data is slightly higher for Californians of color, resulting in a higher relative disparity in exposure.This finding may suggest a population migration of people of color towards lower traffic areas between the 2000 and 2010 Census.Assuming the trend of population migration continued towards 2020, we might expect that the PWM exposure concentrations for each group might decrease further towards 2019.However, the persistence of the relative exposure disparity regardless of population dataset year suggests that the spatial distribution of emissions is not markedly variable over time, thereby leading to relatively constant relative disparities over time.

Fig. S18 .
Fig. S18.Exposure and relative disparity in exposure from non-exhaust mobile-source PM2.5 emissions for each demographic group.Statewide population-weighted mean exposure concentrations (A) and relative disparity in exposure (B) to emissions of only non-exhaust PM2.5 from on-road mobile sources during the study period.The Asian, Black, white, and other racial groups do not include those identifying as Hispanic.Here, we show that the population-weighted mean exposure to emissions of nonexhaust PM2.5 has increased for all groups yet the relative disparity in exposure has remained constant.Notably, although the exposure concentration from non-exhaust sources is only approximately 1% of the current National Ambient Air Quality Standard, the magnitude of the relative disparity in exposure for all groups is larger for non-exhaust PM2.5 than for the full set of emissions shown in Fig.1.While this is not equivalent to modeling the future all-electric fleet, we can use the non-exhaust emissions from the full vehicle fleet as an analog for current driving patterns and emissions that might be relevant to disparities in an all-electric future.This sensitivity analysis suggests that relative disparities may persist even in the "all-electric future" where all vehicles across the fleet have zero exhaust emissions.

Table S1 .
Vehicle fleet classifications for EMFAC2021 vehicle types used in this study.Descriptions and GVWR are from Table6.1 in the EMFAC2021 Volume III Technical Document, Version 1.0.0.b.GVWR is not provided for all vehicle types.

Table S2 .
California's population breakdown by race and ethnicity.

Group b Population Breakdown (%) Statewide AB617 Community c Non-AB617 Community c SB535 Community d Non-SB535 Community
Population data is queried from the 2010 Census at the Census tract level.

Table S4 .
Exposure to PM2.5 from on-road vehicles by AB617 community.

Table S5 .
Exposure and disparity to on-road vehicles by income quartile.Income quartiles are defined as follows.Total population and median household income (adjusted to 2012 inflation) were queried at the block group level from the 2008-2012 American Community Survey.Income data were adjusted to 2010 using the Consumer Price Index.Block groups were then sorted by median income level and the percentile of the overall population was estimated as the cumulative number of people at that income level divided by the total population: < $40,892 Q1, $40,893-$58,452 Q2, $58,453-$82,842 Q3, and >$82,843 Q4.Each quartile represents approximately 9.3 million Californians.b.The population-weighted mean exposure is estimated by multiplying the annual average PM2.5 concentration by the population of the demographic group of interest within that grid cell, summing across all grid cells, and dividing by the total population:  != A positive number indicates a higher than statewide average exposure disparity.Numbers shown here may not subtract correctly due to rounding.d.The absolute disparity for an income group is estimated as the statewide average exposure ("Total") subtracted from the group's exposure: DA,k = PWMk -PWMT e.The relative disparity for an income group is the absolute disparity divided by the statewide average exposure concentration.

Table S6 .
Population-weighted mean exposure by demographic group and vehicle fleet.